<<<<<<< HEAD Pandas Profiling Report

Overview

Dataset statistics

Number of variables22
Number of observations1265
Missing cells16843
Missing cells (%)60.5%
Duplicate rows50
Duplicate rows (%)4.0%
Total size in memory217.5 KiB
Average record size in memory176.1 B

Variable types

Categorical6
Numeric16

Alerts

Dataset has 50 (4.0%) duplicate rowsDuplicates
Material_2 has a high cardinality: 96 distinct valuesHigh cardinality
Additive_1 has a high cardinality: 118 distinct valuesHigh cardinality
Material_Main has 421 (33.3%) missing valuesMissing
Material_2 has 554 (43.8%) missing valuesMissing
Additive_1 has 817 (64.6%) missing valuesMissing
Additive Species has 847 (67.0%) missing valuesMissing
Additive_2 has 1241 (98.1%) missing valuesMissing
Method has 447 (35.3%) missing valuesMissing
Application Rate (%DW) has 988 (78.1%) missing valuesMissing
Initial moisture content (%) has 422 (33.4%) missing valuesMissing
Initial pH has 517 (40.9%) missing valuesMissing
Initial TN (%) has 709 (56.0%) missing valuesMissing
Initial TC (%) has 724 (57.2%) missing valuesMissing
Initial C/N (%) has 365 (28.9%) missing valuesMissing
Time Period has 615 (48.6%) missing valuesMissing
Compost volume (m³) has 1106 (87.4%) missing valuesMissing
Initial density (kg/L) has 1107 (87.5%) missing valuesMissing
Air flow (L·min⁻¹·kg⁻¹) has 982 (77.6%) missing valuesMissing
TN loss (%) has 763 (60.3%) missing valuesMissing
NH3-N loss (%) has 633 (50.0%) missing valuesMissing
N2O-N loss (%) has 806 (63.7%) missing valuesMissing
TC loss (%) has 842 (66.6%) missing valuesMissing
CH4-C loss (%) has 869 (68.7%) missing valuesMissing
CO2-C loss (%) has 1068 (84.4%) missing valuesMissing

Reproduction

Analysis started2024-04-16 06:06:57.486467
Analysis finished2024-04-16 06:07:20.348523
Duration22.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Material_Main
Categorical

Distinct13
Distinct (%)1.5%
Missing421
Missing (%)33.3%
Memory size10.0 KiB
Swine manure
325 
Poultry manure
201 
Cow manure
182 
Manure
44 
Digestate
 
28
Other values (8)
64 

Length

Max length14
Median length13
Mean length11.297393
Min length5

Characters and Unicode

Total characters9535
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSwine manure
2nd rowSwine manure
3rd rowSwine manure
4th rowSwine manure
5th rowCow manure

Common Values

ValueCountFrequency (%)
Swine manure 325
25.7%
Poultry manure 201
15.9%
Cow manure 182
14.4%
Manure 44
 
3.5%
Digestate 28
 
2.2%
OFMSW 25
 
2.0%
Yard Waste 13
 
1.0%
Sludge 10
 
0.8%
Horse manure 5
 
0.4%
Food Waste 5
 
0.4%
Other values (3) 6
 
0.5%
(Missing) 421
33.3%

Length

2024-04-16T14:07:20.416099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Overview

Dataset statistics

Number of variables16
Number of observations1165
Missing cells8199
Missing cells (%)44.0%
Duplicate rows37
Duplicate rows (%)3.2%
Total size in memory145.8 KiB
Average record size in memory128.1 B

Variable types

Categorical4
Numeric12

Alerts

Dataset has 37 (3.2%) duplicate rowsDuplicates
Excipients_1 has a high cardinality: 93 distinct valuesHigh cardinality
Application Rate (%) is highly overall correlated with Excipients_1High correlation
initial TN(%) is highly overall correlated with initial CN(%)High correlation
initial TC(%) is highly overall correlated with CO2-C loss (%)High correlation
initial CN(%) is highly overall correlated with initial TN(%)High correlation
TN loss (%) is highly overall correlated with NH3-N loss (%)High correlation
NH3-N loss (%) is highly overall correlated with TN loss (%)High correlation
N2O-N loss (%) is highly overall correlated with TC loss (%)High correlation
TC loss (%) is highly overall correlated with N2O-N loss (%) and 1 other fieldsHigh correlation
CO2-C loss (%) is highly overall correlated with initial TC(%) and 1 other fieldsHigh correlation
material_0 is highly overall correlated with material_1 and 1 other fieldsHigh correlation
material_1 is highly overall correlated with material_0 and 1 other fieldsHigh correlation
Excipients_1 is highly overall correlated with Application Rate (%) and 2 other fieldsHigh correlation
material_0 has 87 (7.5%) missing valuesMissing
material_1 has 409 (35.1%) missing valuesMissing
Excipients_1 has 498 (42.7%) missing valuesMissing
Additive Species has 810 (69.5%) missing valuesMissing
Application Rate (%) has 948 (81.4%) missing valuesMissing
initial moisture content(%) has 256 (22.0%) missing valuesMissing
initial pH has 306 (26.3%) missing valuesMissing
initial TN(%) has 214 (18.4%) missing valuesMissing
initial TC(%) has 222 (19.1%) missing valuesMissing
initial CN(%) has 262 (22.5%) missing valuesMissing
TN loss (%) has 558 (47.9%) missing valuesMissing
NH3-N loss (%) has 460 (39.5%) missing valuesMissing
N2O-N loss (%) has 631 (54.2%) missing valuesMissing
TC loss (%) has 795 (68.2%) missing valuesMissing
CH4-C loss (%) has 769 (66.0%) missing valuesMissing
CO2-C loss (%) has 974 (83.6%) missing valuesMissing

Reproduction

Analysis started2024-04-14 16:17:25.544506
Analysis finished2024-04-14 16:17:36.295238
Duration10.75 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

material_0
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.6%
Missing87
Missing (%)7.5%
Memory size9.2 KiB
Manure
673 
Sewage sludge
185 
Lignin
87 
Food waste
86 
Digestate
 
43

Length

Max length13
Median length6
Mean length7.6363636
Min length5

Characters and Unicode

Total characters8232
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManure
2nd rowManure
3rd rowManure
4th rowManure
5th rowManure

Common Values

ValueCountFrequency (%)
Manure 673
57.8%
Sewage sludge 185
 
15.9%
Lignin 87
 
7.5%
Food waste 86
 
7.4%
Digestate 43
 
3.7%
Other 4
 
0.3%
(Missing) 87
 
7.5%

Length

2024-04-15T00:17:36.346186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T00:17:36.406667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manure 673
49.9%
sewage 185
 
13.7%
sludge 185
 
13.7%
lignin 87
 
6.4%
food 86
 
6.4%
waste 86
 
6.4%
digestate 43
 
3.2%
other 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1404
17.1%
a 987
12.0%
u 858
10.4%
n 847
10.3%
r 677
8.2%
M 673
8.2%
g 500
 
6.1%
s 314
 
3.8%
w 271
 
3.3%
271
 
3.3%
Other values (11) 1430
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6883
83.6%
Uppercase Letter 1078
 
13.1%
Space Separator 271
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1404
20.4%
a 987
14.3%
u 858
12.5%
n 847
12.3%
r 677
9.8%
g 500
 
7.3%
s 314
 
4.6%
w 271
 
3.9%
d 271
 
3.9%
i 217
 
3.2%
Other values (4) 537
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
M 673
62.4%
S 185
 
17.2%
L 87
 
8.1%
F 86
 
8.0%
D 43
 
4.0%
O 4
 
0.4%
Space Separator
ValueCountFrequency (%)
271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7961
96.7%
Common 271
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1404
17.6%
a 987
12.4%
u 858
10.8%
n 847
10.6%
r 677
8.5%
M 673
8.5%
g 500
 
6.3%
s 314
 
3.9%
w 271
 
3.4%
d 271
 
3.4%
Other values (10) 1159
14.6%
Common
ValueCountFrequency (%)
271
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1404
17.1%
a 987
12.0%
u 858
10.4%
n 847
10.3%
r 677
8.2%
M 673
8.2%
g 500
 
6.1%
s 314
 
3.8%
w 271
 
3.3%
271
 
3.3%
Other values (11) 1430
17.4%

material_1
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)1.7%
Missing409
Missing (%)35.1%
Memory size9.2 KiB
Swine manure
319 
Cow manure
172 
Poultry manure
131 
Manure
43 
Digestate
 
28
Other values (8)
63 

Length

Max length14
Median length13
Mean length11.058201
Min length5

Characters and Unicode

Total characters8360
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSwine manure
2nd rowSwine manure
3rd rowSwine manure
4th rowSwine manure
5th rowCow manure

Common Values

ValueCountFrequency (%)
Swine manure 319
27.4%
Cow manure 172
14.8%
Poultry manure 131
 
11.2%
Manure 43
 
3.7%
Digestate 28
 
2.4%
OFMSW 25
 
2.1%
Yard Waste 13
 
1.1%
Sludge 10
 
0.9%
Horse manure 5
 
0.4%
Food Waste 5
 
0.4%
Other values (3) 5
 
0.4%
(Missing) 409
35.1%

Length

2024-04-15T00:17:36.481354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manure 759
48.1%
swine 325
20.6%
poultry 201
 
12.7%
cow 182
 
11.5%
digestate 28
 
1.8%
ofmsw 25
 
1.6%
waste 18
 
1.1%
yard 13
 
0.8%
sludge 12
 
0.8%
horse 5
 
0.3%
Other values (4) 11
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 1181
12.4%
n 1086
11.4%
r 980
10.3%
u 974
10.2%
a 822
8.6%
735
7.7%
m 717
7.5%
w 509
 
5.3%
o 398
 
4.2%
S 362
 
3.8%
Other values (17) 1771
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7838
82.2%
Uppercase Letter 962
 
10.1%
Space Separator 735
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1181
15.1%
n 1086
13.9%
r 980
12.5%
u 974
12.4%
a 822
10.5%
m 717
9.1%
w 509
6.5%
o 398
 
5.1%
i 353
 
4.5%
t 277
 
3.5%
Other values (6) 541
6.9%
Uppercase Letter
ValueCountFrequency (%)
S 362
37.6%
P 201
20.9%
C 182
18.9%
M 69
 
7.2%
W 43
 
4.5%
F 30
 
3.1%
D 28
 
2.9%
O 27
 
2.8%
Y 13
 
1.4%
H 7
 
0.7%
Space Separator
ValueCountFrequency (%)
735
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8800
92.3%
Common 735
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1181
13.4%
n 1086
12.3%
r 980
11.1%
u 974
11.1%
a 822
9.3%
m 717
8.1%
w 509
 
5.8%
o 398
 
4.5%
S 362
 
4.1%
i 353
 
4.0%
Other values (16) 1418
16.1%
Common
ValueCountFrequency (%)
735
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1181
12.4%
n 1086
11.4%
r 980
10.3%
u 974
10.2%
a 822
8.6%
735
7.7%
m 717
7.5%
w 509
 
5.3%
o 398
 
4.2%
S 362
 
3.8%
Other values (17) 1771
18.6%

Material_2
Categorical

HIGH CARDINALITY  MISSING 

Distinct96
Distinct (%)13.5%
Missing554
Missing (%)43.8%
Memory size10.0 KiB
Corn stalk
133 
Sawdust
125 
Wheat straw
83 
Rice straw
41 
Plant straw
 
24
Other values (91)
305 

Length

Max length55
Median length37
Mean length12.222222
Min length4

Characters and Unicode

Total characters8690
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)5.6%

Sample

1st rowCorn stalk
2nd rowCorn stalk
3rd rowCorn stalk
4th rowCorn stalk
5th rowSawdust

Common Values

ValueCountFrequency (%)
Corn stalk 133
 
10.5%
Sawdust 125
 
9.9%
Wheat straw 83
 
6.6%
Rice straw 41
 
3.2%
Plant straw 24
 
1.9%
Cornstalk 23
 
1.8%
Mushroom residue 17
 
1.3%
Rice husk 13
 
1.0%
Dariy manure + Tomato stalks 11
 
0.9%
Shredded lop + Screenings 10
 
0.8%
Other values (86) 231
18.3%
(Missing) 554
43.8%

Length

2024-04-16T14:07:20.512791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
straw 183
 
12.9%
corn 139
 
9.8%
sawdust 139
 
9.8%
stalk 136
 
9.6%
wheat 91
 
6.4%
rice 67
 
4.7%
49
 
3.5%
manure 39
 
2.7%
residue 34
 
2.4%
waste 34
 
2.4%
Other values (109) 508
35.8%

Most occurring characters

ValueCountFrequency (%)
a 930
 
10.7%
s 804
 
9.3%
t 803
 
9.2%
708
 
8.1%
r 653
 
7.5%
e 614
 
7.1%
w 425
 
4.9%
u 408
 
4.7%
o 391
 
4.5%
n 310
 
3.6%
Other values (34) 2644
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7072
81.4%
Uppercase Letter 785
 
9.0%
Space Separator 708
 
8.1%
Math Symbol 113
 
1.3%
Other Punctuation 8
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 930
13.2%
s 804
11.4%
t 803
11.4%
r 653
9.2%
e 614
8.7%
w 425
 
6.0%
u 408
 
5.8%
o 391
 
5.5%
n 310
 
4.4%
l 295
 
4.2%
Other values (14) 1439
20.3%
Uppercase Letter
ValueCountFrequency (%)
S 249
31.7%
C 172
21.9%
W 92
 
11.7%
R 71
 
9.0%
M 58
 
7.4%
P 49
 
6.2%
T 22
 
2.8%
G 16
 
2.0%
B 15
 
1.9%
F 14
 
1.8%
Other values (5) 27
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 4
50.0%
, 4
50.0%
Space Separator
ValueCountFrequency (%)
708
100.0%
Math Symbol
ValueCountFrequency (%)
+ 113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7857
90.4%
Common 833
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 930
11.8%
s 804
 
10.2%
t 803
 
10.2%
r 653
 
8.3%
e 614
 
7.8%
w 425
 
5.4%
u 408
 
5.2%
o 391
 
5.0%
n 310
 
3.9%
l 295
 
3.8%
Other values (29) 2224
28.3%
Common
ValueCountFrequency (%)
708
85.0%
+ 113
 
13.6%
. 4
 
0.5%
- 4
 
0.5%
, 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 930
 
10.7%
s 804
 
9.3%
t 803
 
9.2%
708
 
8.1%
r 653
 
7.5%
e 614
 
7.1%
w 425
 
4.9%
u 408
 
4.7%
o 391
 
4.5%
n 310
 
3.6%
Other values (34) 2644
30.4%

Additive_1
Categorical

HIGH CARDINALITY  MISSING 

Distinct118
Distinct (%)26.3%
Missing817
Missing (%)64.6%
Memory size10.0 KiB
biochar
54 
PO43- and Mg2+ salts
39 
superphosphate
33 
Biochar
32 
zeolite
27 
Other values (113)
263 

Length

Max length119
Median length38
Mean length14.203125
Min length1

Characters and Unicode

Total characters6363
Distinct characters58
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)14.1%

Sample

1st rowMg(OH)2+H3PO4
2nd rowMg(OH)2+H3PO4
3rd rowMg(OH)2+H3PO4
4th rowgypsum
5th rowgypsum

Common Values

ValueCountFrequency (%)
biochar 54
 
4.3%
PO43- and Mg2+ salts 39
 
3.1%
superphosphate 33
 
2.6%
Biochar 32
 
2.5%
zeolite 27
 
2.1%
phosphogypsum 20
 
1.6%
gypsum 11
 
0.9%
zeolites+calcium superphosphate+ferrous sulfate 9
 
0.7%
microbiological agent 8
 
0.6%
TAT105(a thermophilic,ammonium-tolerant bacterium that grows assimilatingammonium nitrogen and reduces ammoniaemission) 7
 
0.6%
Other values (108) 208
 
16.4%
(Missing) 817
64.6%

Length

2024-04-16T14:07:20.612578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
biochar 96
 
11.9%
and 60
 
7.5%
mg2 39
 
4.9%
salts 39
 
4.9%
superphosphate 39
 
4.9%
po43 39
 
4.9%
zeolite 29
 
3.6%
phosphogypsum 20
 
2.5%
gypsum 17
 
2.1%
sulfate 13
 
1.6%
Other values (144) 413
51.4%

Most occurring characters

ValueCountFrequency (%)
a 557
 
8.8%
e 441
 
6.9%
o 414
 
6.5%
i 394
 
6.2%
s 392
 
6.2%
347
 
5.5%
r 321
 
5.0%
t 311
 
4.9%
h 289
 
4.5%
p 277
 
4.4%
Other values (48) 2620
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5026
79.0%
Uppercase Letter 526
 
8.3%
Space Separator 356
 
5.6%
Decimal Number 275
 
4.3%
Math Symbol 73
 
1.1%
Dash Punctuation 50
 
0.8%
Close Punctuation 19
 
0.3%
Open Punctuation 19
 
0.3%
Other Punctuation 17
 
0.3%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 557
 
11.1%
e 441
 
8.8%
o 414
 
8.2%
i 394
 
7.8%
s 392
 
7.8%
r 321
 
6.4%
t 311
 
6.2%
h 289
 
5.8%
p 277
 
5.5%
l 230
 
4.6%
Other values (15) 1400
27.9%
Uppercase Letter
ValueCountFrequency (%)
O 117
22.2%
P 76
14.4%
H 56
10.6%
M 56
10.6%
B 47
8.9%
C 38
 
7.2%
S 32
 
6.1%
N 28
 
5.3%
T 20
 
3.8%
A 17
 
3.2%
Other values (7) 39
 
7.4%
Decimal Number
ValueCountFrequency (%)
2 98
35.6%
4 92
33.5%
3 60
21.8%
1 9
 
3.3%
0 8
 
2.9%
5 7
 
2.5%
9 1
 
0.4%
Space Separator
ValueCountFrequency (%)
347
97.5%
  9
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 16
94.1%
. 1
 
5.9%
Math Symbol
ValueCountFrequency (%)
+ 73
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5552
87.3%
Common 811
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 557
 
10.0%
e 441
 
7.9%
o 414
 
7.5%
i 394
 
7.1%
s 392
 
7.1%
r 321
 
5.8%
t 311
 
5.6%
h 289
 
5.2%
p 277
 
5.0%
l 230
 
4.1%
Other values (32) 1926
34.7%
Common
ValueCountFrequency (%)
347
42.8%
2 98
 
12.1%
4 92
 
11.3%
+ 73
 
9.0%
3 60
 
7.4%
- 50
 
6.2%
) 19
 
2.3%
( 19
 
2.3%
, 16
 
2.0%
1 9
 
1.1%
Other values (6) 28
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6354
99.9%
None 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 557
 
8.8%
e 441
 
6.9%
o 414
 
6.5%
i 394
 
6.2%
s 392
 
6.2%
347
 
5.5%
r 321
 
5.1%
t 311
 
4.9%
h 289
 
4.5%
p 277
 
4.4%
Other values (47) 2611
41.1%
None
ValueCountFrequency (%)
  9
100.0%

Additive Species
Categorical

Distinct4
Distinct (%)1.0%
Missing847
Missing (%)67.0%
Memory size10.0 KiB
Physical
249 
Chemical
119 
Biological
35 
Mixture
 
15

Length

Max length10
Median length8
Mean length8.1315789
Min length7

Characters and Unicode

Total characters3399
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChemical
2nd rowChemical
3rd rowChemical
4th rowPhysical
5th rowPhysical

Common Values

ValueCountFrequency (%)
Physical 249
 
19.7%
Chemical 119
 
9.4%
Biological 35
 
2.8%
Mixture 15
 
1.2%
(Missing) 847
67.0%

Length

2024-04-16T14:07:20.700147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manure 673
48.0%
swine 319
22.7%
cow 172
 
12.3%
poultry 131
 
9.3%
digestate 28
 
2.0%
ofmsw 25
 
1.8%
waste 18
 
1.3%
yard 13
 
0.9%
sludge 10
 
0.7%
horse 5
 
0.4%
Other values (3) 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 1083
13.0%
n 994
11.9%
r 824
9.9%
u 816
9.8%
a 734
8.8%
650
7.8%
m 631
7.5%
w 491
 
5.9%
S 354
 
4.2%
i 347
 
4.2%
Other values (17) 1436
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6836
81.8%
Uppercase Letter 874
 
10.5%
Space Separator 650
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1083
15.8%
n 994
14.5%
r 824
12.1%
u 816
11.9%
a 734
10.7%
m 631
9.2%
w 491
7.2%
i 347
 
5.1%
o 318
 
4.7%
t 207
 
3.0%
Other values (6) 391
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
S 354
40.5%
C 172
19.7%
P 131
 
15.0%
M 69
 
7.9%
W 43
 
4.9%
F 30
 
3.4%
D 28
 
3.2%
O 27
 
3.1%
Y 13
 
1.5%
H 7
 
0.8%
Space Separator
ValueCountFrequency (%)
650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7710
92.2%
Common 650
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1083
14.0%
n 994
12.9%
r 824
10.7%
u 816
10.6%
a 734
9.5%
m 631
8.2%
w 491
6.4%
S 354
 
4.6%
i 347
 
4.5%
o 318
 
4.1%
Other values (16) 1118
14.5%
Common
ValueCountFrequency (%)
650
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1083
13.0%
n 994
11.9%
r 824
9.9%
u 816
9.8%
a 734
8.8%
650
7.8%
m 631
7.5%
w 491
 
5.9%
S 354
 
4.2%
i 347
 
4.2%
Other values (17) 1436
17.2%

Excipients_1
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct93
Distinct (%)13.9%
Missing498
Missing (%)42.7%
Memory size9.2 KiB
Corn stalk
133 
Sawdust
91 
Wheat straw
76 
Rice straw
40 
Plant straw
 
24
Other values (88)
303 

Length

Max length55
Median length37
Mean length12.242879
Min length4

Characters and Unicode

Total characters8166
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)5.7%

Sample

1st rowCorn stalk
2nd rowCorn stalk
3rd rowCorn stalk
4th rowCorn stalk
5th rowSawdust

Common Values

ValueCountFrequency (%)
Corn stalk 133
 
11.4%
Sawdust 91
 
7.8%
Wheat straw 76
 
6.5%
Rice straw 40
 
3.4%
Plant straw 24
 
2.1%
Cornstalk 23
 
2.0%
Mushroom residue 17
 
1.5%
Rice husk 13
 
1.1%
Sawdust 12
 
1.0%
Dariy manure + Tomato stalks 11
 
0.9%
Other values (83) 227
19.5%
(Missing) 498
42.7%

Length

2024-04-15T00:17:36.559253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
straw 173
 
13.0%
corn 138
 
10.4%
stalk 136
 
10.2%
sawdust 117
 
8.8%
wheat 84
 
6.3%
rice 66
 
5.0%
manure 39
 
2.9%
37
 
2.8%
waste 34
 
2.6%
residue 33
 
2.5%
Other values (105) 472
35.5%

Most occurring characters

ValueCountFrequency (%)
a 891
 
10.9%
t 768
 
9.4%
s 764
 
9.4%
684
 
8.4%
r 621
 
7.6%
e 564
 
6.9%
w 390
 
4.8%
u 385
 
4.7%
o 374
 
4.6%
n 289
 
3.5%
Other values (34) 2436
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6642
81.3%
Uppercase Letter 727
 
8.9%
Space Separator 684
 
8.4%
Math Symbol 101
 
1.2%
Other Punctuation 8
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 891
13.4%
t 768
11.6%
s 764
11.5%
r 621
9.3%
e 564
8.5%
w 390
 
5.9%
u 385
 
5.8%
o 374
 
5.6%
n 289
 
4.4%
l 285
 
4.3%
Other values (14) 1311
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 204
28.1%
C 171
23.5%
W 85
11.7%
R 70
 
9.6%
M 58
 
8.0%
P 49
 
6.7%
T 18
 
2.5%
G 16
 
2.2%
B 15
 
2.1%
F 14
 
1.9%
Other values (5) 27
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 4
50.0%
. 4
50.0%
Space Separator
ValueCountFrequency (%)
684
100.0%
Math Symbol
ValueCountFrequency (%)
+ 101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7369
90.2%
Common 797
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 891
12.1%
t 768
 
10.4%
s 764
 
10.4%
r 621
 
8.4%
e 564
 
7.7%
w 390
 
5.3%
u 385
 
5.2%
o 374
 
5.1%
n 289
 
3.9%
l 285
 
3.9%
Other values (29) 2038
27.7%
Common
ValueCountFrequency (%)
684
85.8%
+ 101
 
12.7%
- 4
 
0.5%
, 4
 
0.5%
. 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 891
 
10.9%
t 768
 
9.4%
s 764
 
9.4%
684
 
8.4%
r 621
 
7.6%
e 564
 
6.9%
w 390
 
4.8%
u 385
 
4.7%
o 374
 
4.6%
n 289
 
3.5%
Other values (34) 2436
29.8%

Additive Species
Categorical

Distinct4
Distinct (%)1.1%
Missing810
Missing (%)69.5%
Memory size9.2 KiB
Physical
227 
Chemical
85 
Biological
28 
Mixture
 
15

Length

Max length10
Median length8
Mean length8.115493
Min length7

Characters and Unicode

Total characters2881
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChemical
2nd rowChemical
3rd rowChemical
4th rowPhysical
5th rowPhysical

Common Values

ValueCountFrequency (%)
Physical 227
 
19.5%
Chemical 85
 
7.3%
Biological 28
 
2.4%
Mixture 15
 
1.3%
(Missing) 810
69.5%

Length

2024-04-15T00:17:36.621958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:07:20.793576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T00:17:36.682822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
physical 249
59.6%
chemical 119
28.5%
biological 35
 
8.4%
mixture 15
 
3.6%

Most occurring characters

ValueCountFrequency (%)
i 453
13.3%
l 438
12.9%
c 403
11.9%
a 403
11.9%
h 368
10.8%
P 249
7.3%
y 249
7.3%
s 249
7.3%
e 134
 
3.9%
C 119
 
3.5%
Other values (9) 334
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2981
87.7%
Uppercase Letter 418
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 453
15.2%
l 438
14.7%
c 403
13.5%
a 403
13.5%
h 368
12.3%
y 249
8.4%
s 249
8.4%
e 134
 
4.5%
m 119
 
4.0%
o 70
 
2.3%
Other values (5) 95
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
P 249
59.6%
C 119
28.5%
B 35
 
8.4%
M 15
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3399
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 453
13.3%
l 438
12.9%
c 403
11.9%
a 403
11.9%
h 368
10.8%
P 249
7.3%
y 249
7.3%
s 249
7.3%
e 134
 
3.9%
C 119
 
3.5%
Other values (9) 334
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 453
13.3%
l 438
12.9%
c 403
11.9%
a 403
11.9%
h 368
10.8%
P 249
7.3%
y 249
7.3%
s 249
7.3%
e 134
 
3.9%
C 119
 
3.5%
Other values (9) 334
9.8%

Additive_2
Categorical

Distinct10
Distinct (%)41.7%
Missing1241
Missing (%)98.1%
Memory size10.0 KiB
zeolite
DCD
Gypsum
Calcium-bontonite
Urea
Other values (5)

Length

Max length18
Median length15
Mean length8.5
Min length3

Characters and Unicode

Total characters204
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)20.8%

Sample

1st rowactivated charcoal
2nd rowManganese ore
3rd rowFerrous Sulfate
4th rowBean dreg
5th rowGypsum

Common Values

ValueCountFrequency (%)
zeolite 6
 
0.5%
DCD 4
 
0.3%
Gypsum 4
 
0.3%
Calcium-bontonite 3
 
0.2%
Urea 2
 
0.2%
activated charcoal 1
 
0.1%
sheep manure 1
 
0.1%
Bean dreg 1
 
0.1%
Manganese ore 1
 
0.1%
Ferrous Sulfate 1
 
0.1%
(Missing) 1241
98.1%

Length

2024-04-16T14:07:20.869870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:07:20.962468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
zeolite 6
20.7%
dcd 4
13.8%
gypsum 4
13.8%
calcium-bontonite 3
10.3%
urea 2
 
6.9%
activated 1
 
3.4%
charcoal 1
 
3.4%
sheep 1
 
3.4%
manure 1
 
3.4%
bean 1
 
3.4%
Other values (5) 5
17.2%

Most occurring characters

ValueCountFrequency (%)
e 28
 
13.7%
o 15
 
7.4%
t 15
 
7.4%
a 14
 
6.9%
i 13
 
6.4%
l 11
 
5.4%
n 10
 
4.9%
u 10
 
4.9%
D 8
 
3.9%
r 8
 
3.9%
Other values (21) 72
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 171
83.8%
Uppercase Letter 25
 
12.3%
Space Separator 5
 
2.5%
Dash Punctuation 3
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28
16.4%
o 15
 
8.8%
t 15
 
8.8%
a 14
 
8.2%
i 13
 
7.6%
l 11
 
6.4%
n 10
 
5.8%
u 10
 
5.8%
r 8
 
4.7%
m 8
 
4.7%
Other values (11) 39
22.8%
Uppercase Letter
ValueCountFrequency (%)
D 8
32.0%
C 7
28.0%
G 4
16.0%
U 2
 
8.0%
B 1
 
4.0%
M 1
 
4.0%
F 1
 
4.0%
S 1
 
4.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196
96.1%
Common 8
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28
14.3%
o 15
 
7.7%
t 15
 
7.7%
a 14
 
7.1%
i 13
 
6.6%
l 11
 
5.6%
n 10
 
5.1%
u 10
 
5.1%
D 8
 
4.1%
r 8
 
4.1%
Other values (19) 64
32.7%
Common
ValueCountFrequency (%)
5
62.5%
- 3
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28
 
13.7%
o 15
 
7.4%
t 15
 
7.4%
a 14
 
6.9%
i 13
 
6.4%
l 11
 
5.4%
n 10
 
4.9%
u 10
 
4.9%
D 8
 
3.9%
r 8
 
3.9%
Other values (21) 72
35.3%

Method
Categorical

Distinct4
Distinct (%)0.5%
Missing447
Missing (%)35.3%
Memory size10.0 KiB
Reactor
545 
Static
214 
Windrow
 
38
Turning
 
21

Length

Max length7
Median length7
Mean length6.7383863
Min length6

Characters and Unicode

Total characters5512
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReactor
2nd rowReactor
3rd rowReactor
4th rowReactor
5th rowReactor

Common Values

ValueCountFrequency (%)
Reactor 545
43.1%
Static 214
 
16.9%
Windrow 38
 
3.0%
Turning 21
 
1.7%
(Missing) 447
35.3%

Length

2024-04-16T14:07:21.070156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:07:21.158702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
reactor 545
66.6%
static 214
 
26.2%
windrow 38
 
4.6%
turning 21
 
2.6%

Most occurring characters

ValueCountFrequency (%)
t 973
17.7%
a 759
13.8%
c 759
13.8%
r 604
11.0%
o 583
10.6%
R 545
9.9%
e 545
9.9%
i 273
 
5.0%
S 214
 
3.9%
n 80
 
1.5%
Other values (6) 177
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4694
85.2%
Uppercase Letter 818
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 973
20.7%
a 759
16.2%
c 759
16.2%
r 604
12.9%
o 583
12.4%
e 545
11.6%
i 273
 
5.8%
n 80
 
1.7%
d 38
 
0.8%
w 38
 
0.8%
Other values (2) 42
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
R 545
66.6%
S 214
 
26.2%
W 38
 
4.6%
T 21
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5512
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 973
17.7%
a 759
13.8%
c 759
13.8%
r 604
11.0%
o 583
10.6%
R 545
9.9%
e 545
9.9%
i 273
 
5.0%
S 214
 
3.9%
n 80
 
1.5%
Other values (6) 177
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 973
17.7%
a 759
13.8%
c 759
13.8%
r 604
11.0%
o 583
10.6%
R 545
9.9%
e 545
9.9%
i 273
 
5.0%
S 214
 
3.9%
n 80
 
1.5%
Other values (6) 177
 
3.2%

Application Rate (%DW)
Real number (ℝ)

Distinct100
Distinct (%)36.1%
Missing988
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean12.961448
Minimum0
Maximum51
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:21.256037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
physical 227
63.9%
chemical 85
 
23.9%
biological 28
 
7.9%
mixture 15
 
4.2%

Most occurring characters

ValueCountFrequency (%)
i 383
13.3%
l 368
12.8%
c 340
11.8%
a 340
11.8%
h 312
10.8%
P 227
7.9%
y 227
7.9%
s 227
7.9%
e 100
 
3.5%
C 85
 
3.0%
Other values (9) 272
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2526
87.7%
Uppercase Letter 355
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 383
15.2%
l 368
14.6%
c 340
13.5%
a 340
13.5%
h 312
12.4%
y 227
9.0%
s 227
9.0%
e 100
 
4.0%
m 85
 
3.4%
o 56
 
2.2%
Other values (5) 88
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
P 227
63.9%
C 85
 
23.9%
B 28
 
7.9%
M 15
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2881
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 383
13.3%
l 368
12.8%
c 340
11.8%
a 340
11.8%
h 312
10.8%
P 227
7.9%
y 227
7.9%
s 227
7.9%
e 100
 
3.5%
C 85
 
3.0%
Other values (9) 272
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 383
13.3%
l 368
12.8%
c 340
11.8%
a 340
11.8%
h 312
10.8%
P 227
7.9%
y 227
7.9%
s 227
7.9%
e 100
 
3.5%
C 85
 
3.0%
Other values (9) 272
9.4%

Application Rate (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)37.8%
Missing948
Missing (%)81.4%
Infinite0
Infinite (%)0.0%
Mean12.439352
Minimum0.002
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:36.738473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q319.3
95-th percentile32.04
Maximum51
Range51
Interquartile range (IQR)14.3

Descriptive statistics

Standard deviation9.5967968
Coefficient of variation (CV)0.74041084
Kurtosis1.558373
Mean12.961448
Median Absolute Deviation (MAD)5
Skewness1.1372145
Sum3590.3211
Variance92.098508
MonotonicityNot monotonic
2024-04-16T14:07:21.358531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.976
Q15
median10
Q318
95-th percentile32.04
Maximum51
Range50.998
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.9579927
Coefficient of variation (CV)0.80052341
Kurtosis1.5824732
Mean12.439352
Median Absolute Deviation (MAD)5.2
Skewness1.2022293
Sum2699.3394
Variance99.161618
MonotonicityNot monotonic
2024-04-15T00:17:36.814765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 40
 
3.2%
5 21
 
1.7%
20 15
 
1.2%
15 14
 
1.1%
23 13
 
1.0%
25 8
 
0.6%
6 7
 
0.6%
14 7
 
0.6%
1 7
 
0.6%
33 6
 
0.5%
Other values (90) 139
 
11.0%
(Missing) 988
78.1%
ValueCountFrequency (%)
0 1
 
0.1%
0.002 1
 
0.1%
0.25 3
0.2%
0.4 3
0.2%
0.6 1
 
0.1%
0.65 3
0.2%
0.88 1
 
0.1%
1 7
0.6%
1.5 1
 
0.1%
1.7 1
 
0.1%
ValueCountFrequency (%)
51 1
 
0.1%
50 1
 
0.1%
47 1
 
0.1%
42 1
 
0.1%
38.9 2
 
0.2%
38 1
 
0.1%
35 1
 
0.1%
33 6
0.5%
31.8 1
 
0.1%
31.3 1
 
0.1%
Distinct306
Distinct (%)36.3%
Missing422
Missing (%)33.4%
Infinite0
Infinite (%)0.0%
Mean65.160242
Minimum40
Maximum89.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:21.457926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 33
 
2.8%
5 19
 
1.6%
23 13
 
1.1%
1 9
 
0.8%
25 6
 
0.5%
33 6
 
0.5%
14 6
 
0.5%
15 6
 
0.5%
20 5
 
0.4%
6 5
 
0.4%
Other values (72) 109
 
9.4%
(Missing) 948
81.4%
ValueCountFrequency (%)
0.002 1
 
0.1%
0.25 3
 
0.3%
0.4 3
 
0.3%
0.409 1
 
0.1%
0.454 1
 
0.1%
0.497 1
 
0.1%
0.88 1
 
0.1%
1 9
0.8%
1.5 1
 
0.1%
1.76 1
 
0.1%
ValueCountFrequency (%)
51 1
 
0.1%
50 1
 
0.1%
47 1
 
0.1%
42 1
 
0.1%
35 1
 
0.1%
33 6
0.5%
31.8 1
 
0.1%
31.3 1
 
0.1%
30 3
0.3%
27 2
 
0.2%
Distinct370
Distinct (%)40.7%
Missing256
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean65.270385
Minimum40
Maximum89.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:36.892957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52.26
Q160
median65
Q370
95-th percentile81.368
Maximum89.8
Range49.8
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3400216
Coefficient of variation (CV)0.12799249
Kurtosis0.52628077
Mean65.160242
Median Absolute Deviation (MAD)5
Skewness0.38830225
Sum54930.084
Variance69.55596
MonotonicityNot monotonic
2024-04-16T14:07:21.593428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52.88
Q160
median65
Q370
95-th percentile81.08
Maximum89.8
Range49.8
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1510012
Coefficient of variation (CV)0.12488054
Kurtosis0.5325882
Mean65.270385
Median Absolute Deviation (MAD)5
Skewness0.3839093
Sum59330.78
Variance66.43882
MonotonicityNot monotonic
2024-04-15T00:17:36.971236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 116
 
9.2%
65 94
 
7.4%
55 19
 
1.5%
70 18
 
1.4%
63 16
 
1.3%
61 10
 
0.8%
66 9
 
0.7%
64 9
 
0.7%
65.4 9
 
0.7%
76.28 9
 
0.7%
Other values (296) 534
42.2%
(Missing) 422
33.4%
ValueCountFrequency (%)
40 2
 
0.2%
43.61 1
 
0.1%
44 1
 
0.1%
45 5
0.4%
45.32 1
 
0.1%
46.3 1
 
0.1%
46.7 1
 
0.1%
46.9 2
 
0.2%
47.5 2
 
0.2%
47.6 2
 
0.2%
ValueCountFrequency (%)
89.8 1
 
0.1%
88.5 7
0.6%
86 1
 
0.1%
85.33 2
 
0.2%
85 3
0.2%
84.89 1
 
0.1%
84.66 2
 
0.2%
84.3 4
0.3%
84.1 1
 
0.1%
84 1
 
0.1%

Initial pH
Real number (ℝ)

Distinct269
Distinct (%)36.0%
Missing517
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean7.4872476
Minimum4.28
Maximum10.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:21.717729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 115
 
9.9%
65 93
 
8.0%
55 21
 
1.8%
70 18
 
1.5%
63 17
 
1.5%
61 10
 
0.9%
64 9
 
0.8%
65.4 9
 
0.8%
76.28 9
 
0.8%
58 9
 
0.8%
Other values (360) 599
51.4%
(Missing) 256
22.0%
ValueCountFrequency (%)
40 2
0.2%
43.61 1
0.1%
44 1
0.1%
45 1
0.1%
45 2
0.2%
45.32 1
0.1%
46.3 1
0.1%
46.7 1
0.1%
46.9 2
0.2%
47.5 2
0.2%
ValueCountFrequency (%)
89.8 1
 
0.1%
88.5 7
0.6%
86 1
 
0.1%
85.33 2
 
0.2%
85 3
0.3%
84.89 1
 
0.1%
84.66 2
 
0.2%
84.3 4
0.3%
84.1 1
 
0.1%
84 1
 
0.1%

initial pH
Real number (ℝ)

Distinct376
Distinct (%)43.8%
Missing306
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean7.5032901
Minimum3.51
Maximum10.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:37.063374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.28
5-th percentile5.7959375
Q16.9
median7.6
Q38.1
95-th percentile8.8
Maximum10.7
Range6.42
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.9415029
Coefficient of variation (CV)0.12574753
Kurtosis0.49520186
Mean7.4872476
Median Absolute Deviation (MAD)0.59
Skewness-0.38509614
Sum5600.4612
Variance0.88642771
MonotonicityNot monotonic
2024-04-16T14:07:21.829561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.51
5-th percentile5.9
Q17.003182
median7.6
Q38.1
95-th percentile8.8
Maximum10.7
Range7.19
Interquartile range (IQR)1.096818

Descriptive statistics

Standard deviation0.90939034
Coefficient of variation (CV)0.12119888
Kurtosis0.99300558
Mean7.5032901
Median Absolute Deviation (MAD)0.52
Skewness-0.50184861
Sum6445.3262
Variance0.82699078
MonotonicityNot monotonic
2024-04-15T00:17:37.135043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 25
 
2.0%
7.8 23
 
1.8%
7.6 22
 
1.7%
6.5 18
 
1.4%
8 17
 
1.3%
7.4 16
 
1.3%
7.60990996 16
 
1.3%
8.2 16
 
1.3%
7.1 14
 
1.1%
6.9 14
 
1.1%
Other values (259) 567
44.8%
(Missing) 517
40.9%
ValueCountFrequency (%)
4.28 1
0.1%
4.42231 1
0.1%
4.43 1
0.1%
4.45 1
0.1%
4.8 1
0.1%
4.81275 1
0.1%
4.92 1
0.1%
5 1
0.1%
5.01 1
0.1%
5.04 1
0.1%
ValueCountFrequency (%)
10.7 1
0.1%
10.32 2
0.2%
10 1
0.1%
9.8 2
0.2%
9.6 1
0.1%
9.4 1
0.1%
9.35 1
0.1%
9.33 1
0.1%
9.2 1
0.1%
9.19 1
0.1%

Initial TN (%)
Real number (ℝ)

Distinct279
Distinct (%)50.2%
Missing709
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean2.5511626
Minimum0.37
Maximum14.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:21.981091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 25
 
2.1%
7.8 23
 
2.0%
7.6 22
 
1.9%
7.1 18
 
1.5%
8 17
 
1.5%
6.5 17
 
1.5%
8.2 16
 
1.4%
7.4 16
 
1.4%
7.845613506 15
 
1.3%
6.9 14
 
1.2%
Other values (366) 676
58.0%
(Missing) 306
26.3%
ValueCountFrequency (%)
3.51 1
0.1%
4.28 1
0.1%
4.42231 1
0.1%
4.43 1
0.1%
4.45 1
0.1%
4.8 1
0.1%
4.81275 1
0.1%
4.92 1
0.1%
5 1
0.1%
5.01 1
0.1%
ValueCountFrequency (%)
10.7 1
0.1%
10.32 2
0.2%
10 1
0.1%
9.8 2
0.2%
9.6 1
0.1%
9.4 1
0.1%
9.35 1
0.1%
9.33 1
0.1%
9.2 1
0.1%
9.19 1
0.1%

initial TN(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct640
Distinct (%)67.3%
Missing214
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean2.34614
Minimum0.37
Maximum11.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:37.210780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile0.90875
Q11.4325
median1.99
Q32.68
95-th percentile7.5
Maximum14.86
Range14.49
Interquartile range (IQR)1.2475

Descriptive statistics

Standard deviation2.1058152
Coefficient of variation (CV)0.82543357
Kurtosis12.313516
Mean2.5511626
Median Absolute Deviation (MAD)0.5946875
Skewness3.214147
Sum1418.4464
Variance4.4344577
MonotonicityNot monotonic
2024-04-16T14:07:22.145080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.04
Q11.6163244
median1.9902175
Q32.6
95-th percentile4.865
Maximum11.58
Range11.21
Interquartile range (IQR)0.98367557

Descriptive statistics

Standard deviation1.461799
Coefficient of variation (CV)0.62306556
Kurtosis11.537899
Mean2.34614
Median Absolute Deviation (MAD)0.47021745
Skewness3.0411443
Sum2231.1791
Variance2.1368563
MonotonicityNot monotonic
2024-04-15T00:17:37.281040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 19
 
1.5%
3.088630033 16
 
1.3%
1.4 14
 
1.1%
2.4 13
 
1.0%
2.1 13
 
1.0%
1.7 10
 
0.8%
1.79 8
 
0.6%
1.8 8
 
0.6%
1.78 8
 
0.6%
2.04 7
 
0.6%
Other values (269) 440
34.8%
(Missing) 709
56.0%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.68 2
0.2%
0.69 3
0.2%
0.7 2
0.2%
0.723 1
 
0.1%
0.77 1
 
0.1%
0.782 1
 
0.1%
0.794 1
 
0.1%
0.8 2
0.2%
0.807 1
 
0.1%
ValueCountFrequency (%)
14.86 2
0.2%
14.56 1
0.1%
14.2 2
0.2%
13.4 1
0.1%
11.58 1
0.1%
11.12 1
0.1%
10.4 1
0.1%
10.27 1
0.1%
10 1
0.1%
9.79 1
0.1%

Initial TC (%)
Real number (ℝ)

Distinct329
Distinct (%)60.8%
Missing724
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean50.04774
Minimum1.45
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:22.306130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.845139719 28
 
2.4%
1.9 19
 
1.6%
2.1 13
 
1.1%
2.4 13
 
1.1%
1.4 12
 
1.0%
1.79 8
 
0.7%
1.5 8
 
0.7%
1.7 7
 
0.6%
2.04 7
 
0.6%
2.6 6
 
0.5%
Other values (630) 830
71.2%
(Missing) 214
 
18.4%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.68 2
0.2%
0.69 3
0.3%
0.7 2
0.2%
0.723 1
 
0.1%
0.77 1
 
0.1%
0.782 1
 
0.1%
0.794 1
 
0.1%
0.8 2
0.2%
0.807 1
 
0.1%
ValueCountFrequency (%)
11.58 1
0.1%
11.12 1
0.1%
10.55498413 1
0.1%
10.4 1
0.1%
10.27 1
0.1%
10 1
0.1%
9.79 1
0.1%
9.72 1
0.1%
9.1 1
0.1%
8.8 1
0.1%

initial TC(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct703
Distinct (%)74.5%
Missing222
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean48.693987
Minimum1.45
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:37.367312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile24.89
Q135.865
median40.5
Q348.99
95-th percentile130
Maximum197
Range195.55
Interquartile range (IQR)13.125

Descriptive statistics

Standard deviation31.585141
Coefficient of variation (CV)0.63110023
Kurtosis7.2979402
Mean50.04774
Median Absolute Deviation (MAD)6.2981439
Skewness2.6572508
Sum27075.828
Variance997.62111
MonotonicityNot monotonic
2024-04-16T14:07:22.440237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile28.010389
Q136.724593
median41.06
Q348.312671
95-th percentile109.54927
Maximum197
Range195.55
Interquartile range (IQR)11.588078

Descriptive statistics

Standard deviation27.229587
Coefficient of variation (CV)0.55919814
Kurtosis9.5182113
Mean48.693987
Median Absolute Deviation (MAD)5.54
Skewness2.9294619
Sum45918.43
Variance741.4504
MonotonicityNot monotonic
2024-04-15T00:17:37.481771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.37320351 17
 
1.3%
38 9
 
0.7%
72.33 8
 
0.6%
37.3 7
 
0.6%
40 7
 
0.6%
36.9 7
 
0.6%
78.27 7
 
0.6%
48.99 7
 
0.6%
37.48 6
 
0.5%
36.7 6
 
0.5%
Other values (319) 460
36.4%
(Missing) 724
57.2%
ValueCountFrequency (%)
1.45 3
0.2%
5.8 1
 
0.1%
5.85 1
 
0.1%
6 1
 
0.1%
6.35 1
 
0.1%
19.14 1
 
0.1%
19.152 1
 
0.1%
19.3 1
 
0.1%
20.2 1
 
0.1%
21.8 1
 
0.1%
ValueCountFrequency (%)
197 1
0.1%
196.75 2
0.2%
185 1
0.1%
180.67 1
0.1%
178.23 1
0.1%
177.9 1
0.1%
174.91 1
0.1%
172.4 1
0.1%
168 1
0.1%
163 1
0.1%

Initial C/N (%)
Real number (ℝ)

Distinct440
Distinct (%)48.9%
Missing365
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean21.956058
Minimum1.1
Maximum55.983471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:22.874246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.40765076 22
 
1.9%
38 9
 
0.8%
72.33 8
 
0.7%
46.6 7
 
0.6%
48.99 7
 
0.6%
40 7
 
0.6%
78.27 7
 
0.6%
37.3 7
 
0.6%
36.9 7
 
0.6%
44.89 6
 
0.5%
Other values (693) 856
73.5%
(Missing) 222
 
19.1%
ValueCountFrequency (%)
1.45 3
0.3%
5.8 1
 
0.1%
5.85 1
 
0.1%
6 1
 
0.1%
6.35 1
 
0.1%
19.14 1
 
0.1%
19.152 1
 
0.1%
19.3 1
 
0.1%
20.2 1
 
0.1%
21.8 1
 
0.1%
ValueCountFrequency (%)
197 1
0.1%
196.75 2
0.2%
185 1
0.1%
180.67 1
0.1%
178.23 1
0.1%
177.9 1
0.1%
174.91 1
0.1%
172.4 1
0.1%
168 1
0.1%
163 1
0.1%

initial CN(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct468
Distinct (%)51.8%
Missing262
Missing (%)22.5%
Infinite0
Infinite (%)0.0%
Mean21.873341
Minimum1.1
Maximum55.983471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:37.579692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile9.48322
Q116
median20.146667
Q326.0075
95-th percentile39.831224
Maximum55.983471
Range54.883471
Interquartile range (IQR)10.0075

Descriptive statistics

Standard deviation9.0972293
Coefficient of variation (CV)0.41433801
Kurtosis1.3064576
Mean21.956058
Median Absolute Deviation (MAD)4.8600628
Skewness0.93394753
Sum19760.452
Variance82.75958
MonotonicityNot monotonic
2024-04-16T14:07:22.958161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile9.5104021
Q116
median20.1
Q325.812899
95-th percentile39.709994
Maximum55.983471
Range54.883471
Interquartile range (IQR)9.8128995

Descriptive statistics

Standard deviation9.0815696
Coefficient of variation (CV)0.41518894
Kurtosis1.3589865
Mean21.873341
Median Absolute Deviation (MAD)4.9
Skewness0.95734524
Sum19751.627
Variance82.474906
MonotonicityNot monotonic
2024-04-15T00:17:37.656868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 66
 
5.2%
30 54
 
4.3%
20 36
 
2.8%
15 25
 
2.0%
18 20
 
1.6%
21 11
 
0.9%
17.8 11
 
0.9%
19 9
 
0.7%
10 8
 
0.6%
32 8
 
0.6%
Other values (430) 652
51.5%
(Missing) 365
28.9%
ValueCountFrequency (%)
1.1 1
 
0.1%
2.536745928 1
 
0.1%
2.901592789 1
 
0.1%
4.43 3
0.2%
4.89 1
 
0.1%
4.916910084 1
 
0.1%
5.22 1
 
0.1%
5.4 1
 
0.1%
6.1 2
0.2%
6.15 1
 
0.1%
ValueCountFrequency (%)
55.98347107 1
0.1%
55 1
0.1%
53.73493976 1
0.1%
53.69565217 1
0.1%
53.37662338 1
0.1%
52.88659794 1
0.1%
52.0212766 1
0.1%
51.70992366 1
0.1%
51.42276423 1
0.1%
51.20879121 1
0.1%

Time Period
Real number (ℝ)

Distinct73
Distinct (%)11.2%
Missing615
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean57.891683
Minimum7
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:23.046165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20
Q131
median49
Q387
95-th percentile124
Maximum185
Range178
Interquartile range (IQR)56

Descriptive statistics

Standard deviation34.580579
Coefficient of variation (CV)0.59733242
Kurtosis0.84026907
Mean57.891683
Median Absolute Deviation (MAD)19
Skewness1.0769941
Sum37629.594
Variance1195.8164
MonotonicityNot monotonic
2024-04-16T14:07:23.129800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 45
 
3.6%
30 33
 
2.6%
42 29
 
2.3%
28 26
 
2.1%
60 26
 
2.1%
20 25
 
2.0%
91 24
 
1.9%
100 24
 
1.9%
87 24
 
1.9%
56 20
 
1.6%
Other values (63) 374
29.6%
(Missing) 615
48.6%
ValueCountFrequency (%)
7 4
 
0.3%
10 4
 
0.3%
12 4
 
0.3%
14 5
 
0.4%
15 10
 
0.8%
18 1
 
0.1%
20 25
2.0%
21 12
0.9%
22 6
 
0.5%
25 4
 
0.3%
ValueCountFrequency (%)
185 4
0.3%
172 1
 
0.1%
168 2
 
0.2%
150 9
0.7%
140 5
0.4%
134 7
0.6%
133 1
 
0.1%
126 1
 
0.1%
124 6
0.5%
123 1
 
0.1%

Compost volume (m³)
Real number (ℝ)

Distinct49
Distinct (%)30.8%
Missing1106
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean1.9677731
Minimum0.0035
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:23.225736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 51
 
4.4%
30 43
 
3.7%
20 29
 
2.5%
18 20
 
1.7%
15 20
 
1.7%
17.8 11
 
0.9%
21 11
 
0.9%
32 10
 
0.9%
19 9
 
0.8%
18.4 8
 
0.7%
Other values (458) 691
59.3%
(Missing) 262
 
22.5%
ValueCountFrequency (%)
1.1 1
 
0.1%
2.536745928 1
 
0.1%
2.901592789 1
 
0.1%
4.43 3
0.3%
4.89 1
 
0.1%
4.916910084 1
 
0.1%
5.22 1
 
0.1%
5.4 1
 
0.1%
6.1 2
0.2%
6.15 1
 
0.1%
ValueCountFrequency (%)
55.98347107 1
0.1%
55 1
0.1%
53.73493976 1
0.1%
53.69565217 1
0.1%
53.37662338 1
0.1%
52.88659794 1
0.1%
52.0212766 1
0.1%
51.70992366 1
0.1%
51.5 1
0.1%
51.20879121 1
0.1%

TN loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct494
Distinct (%)81.4%
Missing558
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean22.970922
Minimum-1
Maximum85.54023
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size9.2 KiB
2024-04-15T00:17:37.717592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0035
5-th percentile0.026
Q10.06
median0.273
Q31.2
95-th percentile10
Maximum63
Range62.9965
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation6.6671858
Coefficient of variation (CV)3.3881884
Kurtosis52.179196
Mean1.9677731
Median Absolute Deviation (MAD)0.243
Skewness6.7201306
Sum312.87592
Variance44.451367
MonotonicityNot monotonic
2024-04-16T14:07:23.311312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.06 23
 
1.8%
1.2 11
 
0.9%
1.37 10
 
0.8%
0.09 9
 
0.7%
0.03 8
 
0.6%
0.1 7
 
0.6%
1.12 6
 
0.5%
1 6
 
0.5%
0.01 5
 
0.4%
10 5
 
0.4%
Other values (39) 69
 
5.5%
(Missing) 1106
87.4%
ValueCountFrequency (%)
0.0035 2
 
0.2%
0.01 5
 
0.4%
0.026 3
 
0.2%
0.03 8
 
0.6%
0.0318 1
 
0.1%
0.032 1
 
0.1%
0.0488 1
 
0.1%
0.049 1
 
0.1%
0.05 4
 
0.3%
0.06 23
1.8%
ValueCountFrequency (%)
63 1
 
0.1%
36 2
 
0.2%
11.25 2
 
0.2%
10 5
0.4%
9.75 2
 
0.2%
4.5 1
 
0.1%
4.070657862 2
 
0.2%
2 1
 
0.1%
1.58 1
 
0.1%
1.567 1
 
0.1%

Initial density (kg/L)
Real number (ℝ)

Distinct75
Distinct (%)47.5%
Missing1107
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean0.4556227
Minimum0.05
Maximum1.105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:23.414397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.123
Q10.3
median0.442
Q30.555
95-th percentile0.8866
Maximum1.105
Range1.055
Interquartile range (IQR)0.255

Descriptive statistics

Standard deviation0.23270255
Coefficient of variation (CV)0.51073519
Kurtosis0.0083959325
Mean0.4556227
Median Absolute Deviation (MAD)0.142
Skewness0.60730325
Sum71.988386
Variance0.054150475
MonotonicityNot monotonic
2024-04-16T14:07:23.516511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 17
 
1.3%
0.305 8
 
0.6%
0.3 8
 
0.6%
0.4 6
 
0.5%
0.123 6
 
0.5%
0.25 5
 
0.4%
0.646 5
 
0.4%
0.532 4
 
0.3%
0.667 4
 
0.3%
0.1715 4
 
0.3%
Other values (65) 91
 
7.2%
(Missing) 1107
87.5%
ValueCountFrequency (%)
0.05 2
 
0.2%
0.073 2
 
0.2%
0.099 2
 
0.2%
0.11 1
 
0.1%
0.123 6
0.5%
0.14 2
 
0.2%
0.1715 4
0.3%
0.229 4
0.3%
0.23 1
 
0.1%
0.235 1
 
0.1%
ValueCountFrequency (%)
1.105 1
0.1%
1.061 2
0.2%
1.033 1
0.1%
0.95 1
0.1%
0.92 1
0.1%
0.891 1
0.1%
0.89 1
0.1%
0.886 1
0.1%
0.87 1
0.1%
0.857 2
0.2%
Distinct84
Distinct (%)29.7%
Missing982
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean4.3296437
Minimum0.0058
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:23.615775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0058
5-th percentile0.08
Q10.2
median0.38
Q31.82
95-th percentile35
Maximum35
Range34.9942
Interquartile range (IQR)1.62

Descriptive statistics

Standard deviation9.9720254
Coefficient of variation (CV)2.3031977
Kurtosis4.9550193
Mean4.3296437
Median Absolute Deviation (MAD)0.272
Skewness2.5587319
Sum1225.2892
Variance99.441291
MonotonicityNot monotonic
2024-04-16T14:07:23.703169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 30
 
2.4%
35 24
 
1.9%
0.123 14
 
1.1%
0.48 10
 
0.8%
0.4 9
 
0.7%
0.3 9
 
0.7%
0.108 8
 
0.6%
0.1 8
 
0.6%
0.38 7
 
0.6%
0.455 6
 
0.5%
Other values (74) 158
 
12.5%
(Missing) 982
77.6%
ValueCountFrequency (%)
0.0058 1
 
0.1%
0.0275 3
0.2%
0.035 3
0.2%
0.047 1
 
0.1%
0.05 2
0.2%
0.066 1
 
0.1%
0.07 3
0.2%
0.08 4
0.3%
0.0824 1
 
0.1%
0.085 1
 
0.1%
ValueCountFrequency (%)
35 24
1.9%
21.26 1
 
0.1%
21 5
 
0.4%
16.47 2
 
0.2%
8.68 1
 
0.1%
6.67 2
 
0.2%
6.33 5
 
0.4%
5.087809648 5
 
0.4%
4.29 2
 
0.2%
3.3 1
 
0.1%

TN loss (%)
Real number (ℝ)

Distinct374
Distinct (%)74.5%
Missing763
Missing (%)60.3%
Infinite0
Infinite (%)0.0%
Mean28.488392
Minimum-1
Maximum90.5
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size10.0 KiB
2024-04-16T14:07:23.800704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2.2215
Q115.35
median25.91
Q337.5
95-th percentile70.73
Maximum90.5
Range91.5
Interquartile range (IQR)22.15

Descriptive statistics

Standard deviation19.298012
Coefficient of variation (CV)0.67739912
Kurtosis1.2992112
Mean28.488392
Median Absolute Deviation (MAD)11
Skewness1.0784325
Sum14301.173
Variance372.41326
MonotonicityNot monotonic
2024-04-16T14:07:23.884721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2.7425561
Q111.004942
median20.85
Q332.140076
95-th percentile49.597
Maximum85.54023
Range86.54023
Interquartile range (IQR)21.135134

Descriptive statistics

Standard deviation15.101342
Coefficient of variation (CV)0.65741121
Kurtosis0.23315924
Mean22.970922
Median Absolute Deviation (MAD)10.63
Skewness0.68239566
Sum13943.35
Variance228.05052
MonotonicityNot monotonic
2024-04-15T00:17:37.793896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 6
 
0.5%
15.5 5
 
0.4%
35 5
 
0.4%
0.5 4
 
0.3%
16.99 4
 
0.3%
23 4
 
0.3%
63 4
 
0.3%
27 4
 
0.3%
37.5 3
 
0.2%
48 3
 
0.2%
Other values (364) 460
36.4%
(Missing) 763
60.3%
ValueCountFrequency (%)
-1 1
0.1%
0.2 1
0.1%
0.28 1
0.1%
0.3 1
0.1%
0.34 1
0.1%
0.37 1
0.1%
0.38 1
0.1%
0.39 1
0.1%
0.4 1
0.1%
0.414 1
0.1%
ValueCountFrequency (%)
90.5 1
 
0.1%
88.17 2
0.2%
87.42 2
0.2%
87.12 2
0.2%
86.8 2
0.2%
85.54022989 1
 
0.1%
83.93 3
0.2%
82.85 2
0.2%
82.72 3
0.2%
82 1
 
0.1%

NH3-N loss (%)
Real number (ℝ)

Distinct562
Distinct (%)88.9%
Missing633
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean12.268224
Minimum0.02
Maximum160.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:23.971245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 6
 
0.5%
15.5 5
 
0.4%
35 5
 
0.4%
16.99 4
 
0.3%
27 4
 
0.3%
23 4
 
0.3%
0.5 4
 
0.3%
63 4
 
0.3%
29.9 3
 
0.3%
21 3
 
0.3%
Other values (484) 565
48.5%
(Missing) 558
47.9%
ValueCountFrequency (%)
-1 1
0.1%
0.2 1
0.1%
0.28 1
0.1%
0.3 1
0.1%
0.34 1
0.1%
0.37 1
0.1%
0.38 1
0.1%
0.39 1
0.1%
0.4 1
0.1%
0.414 1
0.1%
ValueCountFrequency (%)
85.54022989 1
 
0.1%
82 1
 
0.1%
72 1
 
0.1%
63 4
0.3%
60 1
 
0.1%
58.15 2
0.2%
58 1
 
0.1%
58 1
 
0.1%
56.79 1
 
0.1%
56 1
 
0.1%

NH3-N loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct631
Distinct (%)89.5%
Missing460
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean12.774377
Minimum0.02
Maximum160.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:37.897028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.17450053
Q11.37922
median9.8
Q318.55
95-th percentile33.0955
Maximum160.6
Range160.58
Interquartile range (IQR)17.17078

Descriptive statistics

Standard deviation13.677587
Coefficient of variation (CV)1.1148792
Kurtosis24.61278
Mean12.268224
Median Absolute Deviation (MAD)8.591
Skewness3.2707045
Sum7753.5175
Variance187.07639
MonotonicityNot monotonic
2024-04-16T14:07:24.054461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.18295868
Q12
median11.126062
Q319.6
95-th percentile32.66
Maximum160.6
Range160.58
Interquartile range (IQR)17.6

Descriptive statistics

Standard deviation13.134421
Coefficient of variation (CV)1.0281849
Kurtosis25.587039
Mean12.774377
Median Absolute Deviation (MAD)8.873938
Skewness3.2135879
Sum9005.9358
Variance172.51302
MonotonicityNot monotonic
2024-04-15T00:17:37.968543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.9 5
 
0.4%
0.11 4
 
0.3%
0.98493 3
 
0.2%
22 3
 
0.2%
38.7 3
 
0.2%
12.7 3
 
0.2%
11.11 3
 
0.2%
21.2 3
 
0.2%
9 3
 
0.2%
0.25 3
 
0.2%
Other values (552) 599
47.4%
(Missing) 633
50.0%
ValueCountFrequency (%)
0.02 1
 
0.1%
0.0354 1
 
0.1%
0.04 1
 
0.1%
0.0413 1
 
0.1%
0.06 1
 
0.1%
0.1 1
 
0.1%
0.11 4
0.3%
0.1137694225 1
 
0.1%
0.1159278 1
 
0.1%
0.12155 1
 
0.1%
ValueCountFrequency (%)
160.6 1
0.1%
84.51497682 1
0.1%
75.5 1
0.1%
74.71149316 1
0.1%
74.5876815 1
0.1%
72.7944032 1
0.1%
65 1
0.1%
52.92898646 1
0.1%
50.9 1
0.1%
48.9 1
0.1%

N2O-N loss (%)
Real number (ℝ)

Distinct321
Distinct (%)69.9%
Missing806
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean1.3227109
Minimum-0.5
Maximum19
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.2%
Memory size10.0 KiB
2024-04-16T14:07:24.143497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.9 5
 
0.4%
0.11 4
 
0.3%
0.98493 3
 
0.3%
12.7 3
 
0.3%
21.16 3
 
0.3%
38.7 3
 
0.3%
22 3
 
0.3%
21.2 3
 
0.3%
9.6 3
 
0.3%
0.25 3
 
0.3%
Other values (621) 672
57.7%
(Missing) 460
39.5%
ValueCountFrequency (%)
0.02 1
 
0.1%
0.0354 1
 
0.1%
0.04 1
 
0.1%
0.0413 1
 
0.1%
0.06 1
 
0.1%
0.1 1
 
0.1%
0.11 4
0.3%
0.1137694225 1
 
0.1%
0.1159278 1
 
0.1%
0.12155 1
 
0.1%
ValueCountFrequency (%)
160.6 1
0.1%
84.51497682 1
0.1%
75.5 1
0.1%
74.71149316 1
0.1%
74.5876815 1
0.1%
72.7944032 1
0.1%
65 1
0.1%
52.92898646 1
0.1%
50.9 1
0.1%
48.9 1
0.1%

N2O-N loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct398
Distinct (%)74.5%
Missing631
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean1.6451723
Minimum-0.5
Maximum19
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.3%
Memory size9.2 KiB
2024-04-15T00:17:38.038319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0.019966
Q10.1872
median0.57
Q31.4
95-th percentile6.089
Maximum19
Range19.5
Interquartile range (IQR)1.2128

Descriptive statistics

Standard deviation2.2654415
Coefficient of variation (CV)1.7127262
Kurtosis15.099479
Mean1.3227109
Median Absolute Deviation (MAD)0.43
Skewness3.5054561
Sum607.12428
Variance5.1322254
MonotonicityNot monotonic
2024-04-16T14:07:24.224622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0.022015
Q10.22859375
median0.715
Q31.976
95-th percentile7.263606
Maximum19
Range19.5
Interquartile range (IQR)1.7474063

Descriptive statistics

Standard deviation2.4258038
Coefficient of variation (CV)1.4744983
Kurtosis8.7544843
Mean1.6451723
Median Absolute Deviation (MAD)0.575
Skewness2.6552344
Sum878.52199
Variance5.884524
MonotonicityNot monotonic
2024-04-15T00:17:38.121872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 10
 
0.8%
0.6 8
 
0.6%
2 8
 
0.6%
1.4 8
 
0.6%
1 6
 
0.5%
0.8 6
 
0.5%
1.5 6
 
0.5%
1.2 5
 
0.4%
0.4 5
 
0.4%
0.02 4
 
0.3%
Other values (311) 393
31.1%
(Missing) 806
63.7%
ValueCountFrequency (%)
-0.5 3
0.2%
0.0006 1
 
0.1%
0.00068 1
 
0.1%
0.000756 1
 
0.1%
0.00189 1
 
0.1%
0.00234 1
 
0.1%
0.0025 1
 
0.1%
0.003 1
 
0.1%
0.004 1
 
0.1%
0.006 1
 
0.1%
ValueCountFrequency (%)
19 1
0.1%
13.05 1
0.1%
12.65063291 1
0.1%
12 1
0.1%
11.22 1
0.1%
11 1
0.1%
10.1722 1
0.1%
9.9 1
0.1%
9.3 1
0.1%
9.103448276 1
0.1%

TC loss (%)
Real number (ℝ)

Distinct301
Distinct (%)71.2%
Missing842
Missing (%)66.6%
Infinite0
Infinite (%)0.0%
Mean42.866484
Minimum5.1
Maximum92.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:24.310980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 10
 
0.9%
0.6 8
 
0.7%
2 8
 
0.7%
0.8 6
 
0.5%
1 6
 
0.5%
1.5 6
 
0.5%
1.4 5
 
0.4%
1.2 5
 
0.4%
0.4 5
 
0.4%
0.19 4
 
0.3%
Other values (388) 471
40.4%
(Missing) 631
54.2%
ValueCountFrequency (%)
-0.5 3
0.3%
0.0006 1
 
0.1%
0.00068 1
 
0.1%
0.000756 1
 
0.1%
0.00189 1
 
0.1%
0.00234 1
 
0.1%
0.0025 1
 
0.1%
0.003 1
 
0.1%
0.004 1
 
0.1%
0.006 1
 
0.1%
ValueCountFrequency (%)
19 1
0.1%
13.05 1
0.1%
12.65063291 1
0.1%
12 1
0.1%
11.22 1
0.1%
11 1
0.1%
10.1722 1
0.1%
9.9 1
0.1%
9.3 1
0.1%
9.103448276 1
0.1%

TC loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct264
Distinct (%)71.4%
Missing795
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean40.571602
Minimum5.1
Maximum92.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:38.204260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile10.3135
Q125.475
median43.785
Q352.8975
95-th percentile65.69
Maximum92.58
Range87.48
Interquartile range (IQR)27.4225

Descriptive statistics

Standard deviation17.615356
Coefficient of variation (CV)0.43417945
Kurtosis-0.46904485
Mean40.571602
Median Absolute Deviation (MAD)11.73
Skewness-0.11069083
Sum15011.493
Variance310.30077
MonotonicityNot monotonic
2024-04-15T00:17:38.276258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.7%
44 5
 
0.4%
51 5
 
0.4%
37.8 4
 
0.3%
40 4
 
0.3%
54 4
 
0.3%
32 4
 
0.3%
53 4
 
0.3%
59.23 4
 
0.3%
43 4
 
0.3%
Other values (254) 324
27.8%
(Missing) 795
68.2%
ValueCountFrequency (%)
5.1 1
0.1%
5.54 1
0.1%
7 1
0.1%
7.04 1
0.1%
7.56 1
0.1%
7.81 1
0.1%
7.9 1
0.1%
8.1 1
0.1%
8.29 1
0.1%
8.3 1
0.1%
ValueCountFrequency (%)
92.58 1
0.1%
88.83 1
0.1%
88.75 1
0.1%
85.83 1
0.1%
80 1
0.1%
79.33 1
0.1%
75.43 1
0.1%
75 1
0.1%
69.1 1
0.1%
68.8 1
0.1%

CH4-C loss (%)
Real number (ℝ)

Distinct277
Distinct (%)69.9%
Missing769
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean1.0944841
Minimum0.001166
Maximum35.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2024-04-15T00:17:38.356477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001166
5-th percentile0.0295
Q10.147525
median0.34
Q30.77375
95-th percentile4.5436182
Maximum35.87
Range35.868834
Interquartile range (IQR)0.626225

Descriptive statistics

Standard deviation2.8612458
Coefficient of variation (CV)2.6142415
Kurtosis77.507749
Mean1.0944841
Median Absolute Deviation (MAD)0.24
Skewness7.7826309
Sum433.41571
Variance8.1867274
MonotonicityNot monotonic
2024-04-15T00:17:38.425873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 8
 
0.7%
0.05 7
 
0.6%
0.4 6
 
0.5%
0.12 6
 
0.5%
0.8 6
 
0.5%
0.1 6
 
0.5%
0.13 5
 
0.4%
0.3 5
 
0.4%
0.7 5
 
0.4%
0.27 4
 
0.3%
Other values (267) 338
29.0%
(Missing) 769
66.0%
ValueCountFrequency (%)
0.001166 1
 
0.1%
0.0012 1
 
0.1%
0.001337 1
 
0.1%
0.0018 1
 
0.1%
0.002 1
 
0.1%
0.002336 1
 
0.1%
0.003016 1
 
0.1%
0.008 1
 
0.1%
0.0098 1
 
0.1%
0.01 3
0.3%
ValueCountFrequency (%)
35.87 1
0.1%
25.2 1
0.1%
24.04 1
0.1%
10 1
0.1%
8.409 1
0.1%
7.5 2
0.2%
7.21 1
0.1%
7.2 2
0.2%
6.4 2
0.2%
6.134185304 1
0.1%

CO2-C loss (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct176
Distinct (%)92.1%
Missing974
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean17.233242
Minimum-0.8
Maximum84.00333
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.3%
Memory size9.2 KiB
2024-04-15T00:17:38.502863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile10.402
Q128.85
median44.71
Q354.53
95-th percentile74.74
Maximum92.58
Range87.48
Interquartile range (IQR)25.68

Descriptive statistics

Standard deviation18.823602
Coefficient of variation (CV)0.43912166
Kurtosis-0.42587372
Mean42.866484
Median Absolute Deviation (MAD)11.86
Skewness-0.0087225121
Sum18132.523
Variance354.32799
MonotonicityNot monotonic
2024-04-16T14:07:24.388623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile0.549
Q13.3473599
median12.135153
Q327.095
95-th percentile47.06
Maximum84.00333
Range84.80333
Interquartile range (IQR)23.74764

Descriptive statistics

Standard deviation17.14616
Coefficient of variation (CV)0.99494686
Kurtosis1.8962755
Mean17.233242
Median Absolute Deviation (MAD)9.4671527
Skewness1.3869988
Sum3291.5492
Variance293.99079
MonotonicityNot monotonic
2024-04-15T00:17:38.579980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.6%
51 5
 
0.4%
44 5
 
0.4%
32 4
 
0.3%
55 4
 
0.3%
37.8 4
 
0.3%
43 4
 
0.3%
40 4
 
0.3%
59.23 4
 
0.3%
54 4
 
0.3%
Other values (291) 377
29.8%
(Missing) 842
66.6%
ValueCountFrequency (%)
5.1 1
0.1%
5.54 1
0.1%
7 1
0.1%
7.04 1
0.1%
7.56 1
0.1%
7.81 1
0.1%
7.9 1
0.1%
8.1 1
0.1%
8.29 1
0.1%
8.3 1
0.1%
ValueCountFrequency (%)
92.58 1
 
0.1%
90.5 1
 
0.1%
88.83 1
 
0.1%
88.75 1
 
0.1%
85.83 1
 
0.1%
83.52 2
0.2%
81.94 3
0.2%
80.91 2
0.2%
80.45 2
0.2%
80 1
 
0.1%

CH4-C loss (%)
Real number (ℝ)

Distinct271
Distinct (%)68.4%
Missing869
Missing (%)68.7%
Infinite0
Infinite (%)0.0%
Mean1.0944841
Minimum0.001166
Maximum35.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-04-16T14:07:24.465527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 2
 
0.2%
32.1 2
 
0.2%
33.3 2
 
0.2%
42.9 2
 
0.2%
45.6 2
 
0.2%
23.9 2
 
0.2%
44.8 2
 
0.2%
42.4 2
 
0.2%
37.2 2
 
0.2%
-0.16 2
 
0.2%
Other values (166) 171
 
14.7%
(Missing) 974
83.6%
ValueCountFrequency (%)
-0.8 1
0.1%
-0.16 2
0.2%
0.1 2
0.2%
0.233 1
0.1%
0.235 1
0.1%
0.293 1
0.1%
0.4537795 1
0.1%
0.47 1
0.1%
0.628 1
0.1%
0.6851181 1
0.1%
ValueCountFrequency (%)
84.00333 1
0.1%
79.56667 1
0.1%
74.43333 1
0.1%
69.3 1
0.1%
60.2 1
0.1%
59 1
0.1%
57.97 1
0.1%
56.949 1
0.1%
52.98333 1
0.1%
48.02 1
0.1%

Interactions

2024-04-15T00:17:34.943097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001166
5-th percentile0.0295
Q10.147525
median0.34
Q30.77375
95-th percentile4.5436182
Maximum35.87
Range35.868834
Interquartile range (IQR)0.626225

Descriptive statistics

Standard deviation2.8612458
Coefficient of variation (CV)2.6142415
Kurtosis77.507749
Mean1.0944841
Median Absolute Deviation (MAD)0.24
Skewness7.7826309
Sum433.41571
Variance8.1867274
MonotonicityNot monotonic
2024-04-16T14:07:24.545312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 8
 
0.6%
0.05 7
 
0.6%
0.34 7
 
0.6%
0.8 6
 
0.5%
0.4 6
 
0.5%
0.1 6
 
0.5%
0.12 6
 
0.5%
0.7 5
 
0.4%
0.3 5
 
0.4%
0.17 5
 
0.4%
Other values (261) 335
 
26.5%
(Missing) 869
68.7%
ValueCountFrequency (%)
0.001166 1
 
0.1%
0.0012 1
 
0.1%
0.001337 1
 
0.1%
0.0018 1
 
0.1%
0.002 1
 
0.1%
0.002336 1
 
0.1%
0.003016 1
 
0.1%
0.008 1
 
0.1%
0.0098 1
 
0.1%
0.01 3
0.2%
ValueCountFrequency (%)
35.87 1
0.1%
25.2 1
0.1%
24.04 1
0.1%
10 1
0.1%
8.409 1
0.1%
7.5 2
0.2%
7.21 1
0.1%
7.2 2
0.2%
6.4 2
0.2%
6.134185304 1
0.1%

CO2-C loss (%)
Real number (ℝ)

Distinct182
Distinct (%)92.4%
Missing1068
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean17.814463
Minimum-0.8
Maximum84.00333
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.2%
Memory size10.0 KiB
2024-04-16T14:07:24.630197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile0.5964
Q13.665
median13.1
Q329
95-th percentile46.484
Maximum84.00333
Range84.80333
Interquartile range (IQR)25.335

Descriptive statistics

Standard deviation17.201808
Coefficient of variation (CV)0.96560914
Kurtosis1.5480454
Mean17.814463
Median Absolute Deviation (MAD)10.6
Skewness1.2720508
Sum3509.4492
Variance295.9022
MonotonicityNot monotonic
2024-04-16T14:07:24.704991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 2
 
0.2%
0.1 2
 
0.2%
23.9 2
 
0.2%
31.4 2
 
0.2%
25 2
 
0.2%
42.9 2
 
0.2%
13.9 2
 
0.2%
33.3 2
 
0.2%
42.4 2
 
0.2%
-0.16 2
 
0.2%
Other values (172) 177
 
14.0%
(Missing) 1068
84.4%
ValueCountFrequency (%)
-0.8 1
0.1%
-0.16 2
0.2%
0.1 2
0.2%
0.233 1
0.1%
0.235 1
0.1%
0.293 1
0.1%
0.4537795 1
0.1%
0.47 1
0.1%
0.628 1
0.1%
0.6851181 1
0.1%
ValueCountFrequency (%)
84.00333 1
0.1%
79.56667 1
0.1%
74.43333 1
0.1%
69.3 1
0.1%
60.2 1
0.1%
59 1
0.1%
57.97 1
0.1%
56.949 1
0.1%
52.98333 1
0.1%
48.02 1
0.1%

Interactions

2024-04-16T14:07:18.138136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:58.592218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.058554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.741589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.747606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:00.895284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.580200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:02.119371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.488432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:04.042082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.270557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.504828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.081737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.683882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.875421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:08.020233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:09.101568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:10.200861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.549607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.799343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.806255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:14.111828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.649283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.408461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.427025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.903500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.164260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:18.217648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:35.003266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:58.689462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.105468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.800840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.822787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.638583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:00.950853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:02.203949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.551148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:04.136138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.329911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.578948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.155070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.896785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:08.084800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:09.161880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:10.268911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.617570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.919254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.874228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.868065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:14.193352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.712073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.483704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.496942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.978128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.232338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:18.296722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:35.078588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:58.790519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.163996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.873741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.898782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:01.015754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.828858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.628674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:02.304845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:04.247166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.399604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.660900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.227752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.986870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.968225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:08.150062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:09.224996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:10.334820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.689256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.954808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-04-16T14:07:13.670451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.024832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.564876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.501438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:17.746267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.471855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.286216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.103428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.872417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.628323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:19.189006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:35.587889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.510262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.496252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:00.635263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.315859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:01.772757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.253740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:03.590052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.049908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.214795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.849482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.410773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.641399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:07.727794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:08.834677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:09.907171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.277185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.512127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.551140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:13.769697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.386196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.114574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.170886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.584961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.938728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:17.826869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:19.265684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.565239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.708184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:35.648041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.547353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:00.708592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.379166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:01.864074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.314936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:03.709158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.114929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.291192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.898171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.483050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.710633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:07.801579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:08.903144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:09.990390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.344093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.593512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.618394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:13.860942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.471390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.193264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.240075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.671055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.996755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:17.902544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.770664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:19.336971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:35.698780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.626549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.608472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:00.767034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.433293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:01.945686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.365361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:03.813455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.169430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.362214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.961123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.548922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.772165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:07.875684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.687847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:08.965844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:10.053821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.411140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.663842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.531931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:13.938447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.262886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.297865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.753917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.048458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:17.978687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.827714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:19.408149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:35.754199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:06:59.684428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:26.684936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:00.831761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:27.516412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:02.035821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:28.427860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:03.931071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:29.232633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:05.436515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.012986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:06.618489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:30.828749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:07.953273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:09.034052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:10.127864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:11.473067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:31.744438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:12.732830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:14.031778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:32.582557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:15.339891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:33.370593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:16.831916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.102527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-16T14:07:18.060133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-04-15T00:17:34.891065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2024-04-16T14:07:19.526469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2024-04-15T00:17:38.649817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Application Rate (%)initial moisture content(%)initial pHinitial TN(%)initial TC(%)initial CN(%)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)material_0material_1Excipients_1Additive Species
Application Rate (%)1.0000.113-0.036-0.349-0.2490.121-0.0060.0260.2420.147-0.1450.3230.2160.2840.5570.230
initial moisture content(%)0.1131.0000.1230.1510.072-0.2040.3020.049-0.0120.3240.097-0.3260.1610.1990.2770.287
initial pH-0.0360.1231.000-0.0910.0370.024-0.052-0.001-0.1230.0130.0030.0090.2080.1050.3040.141
initial TN(%)-0.3490.151-0.0911.0000.278-0.6690.251-0.061-0.2430.001-0.042-0.1720.1220.1560.2340.084
initial TC(%)-0.2490.0720.0370.2781.0000.187-0.258-0.290-0.217-0.180-0.007-0.5310.1100.1080.2470.091
initial CN(%)0.121-0.2040.024-0.6690.1871.000-0.2310.1320.285-0.0750.068-0.1310.1720.2010.4050.238
TN loss (%)-0.0060.302-0.0520.251-0.258-0.2311.0000.6720.4520.493-0.0560.4040.1340.1720.3840.138
NH3-N loss (%)0.0260.049-0.001-0.061-0.2900.1320.6721.0000.3960.2870.0630.4140.0000.1130.3850.141
N2O-N loss (%)0.242-0.012-0.123-0.243-0.2170.2850.4520.3961.0000.5120.3140.3150.1240.0910.2640.158
TC loss (%)0.1470.3240.0130.001-0.180-0.0750.4930.2870.5121.0000.2210.6350.1510.2240.4940.300
CH4-C loss (%)-0.1450.0970.003-0.042-0.0070.068-0.0560.0630.3140.2211.0000.1140.0000.0000.0640.340
CO2-C loss (%)0.323-0.3260.009-0.172-0.531-0.1310.4040.4140.3150.6350.1141.0000.2650.3510.3710.276
material_00.2160.1610.2080.1220.1100.1720.1340.0000.1240.1510.0000.2651.0000.9950.7280.111
material_10.2840.1990.1050.1560.1080.2010.1720.1130.0910.2240.0000.3510.9951.0000.7330.141
Excipients_10.5570.2770.3040.2340.2470.4050.3840.3850.2640.4940.0640.3710.7280.7331.0000.393
Additive Species0.2300.2870.1410.0840.0910.2380.1380.1410.1580.3000.3400.2760.1110.1410.3931.000

Missing values

2024-04-15T00:17:35.843065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T14:07:19.763863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-15T00:17:35.960685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-16T14:07:20.032140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-15T00:17:36.125202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Material_MainMaterial_2Additive_1Additive SpeciesAdditive_2MethodApplication Rate (%DW)Initial moisture content (%)Initial pHInitial TN (%)Initial TC (%)Initial C/N (%)Time PeriodCompost volume (m³)Initial density (kg/L)Air flow (L·min⁻¹·kg⁻¹)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)
0Swine manureCorn stalkNaNNaNNaNReactorNaN65.07.606642.66039.9015.00000026.00.050.5320.5735.028.00000NaN50.0NaNNaN
1Swine manureCorn stalkMg(OH)2+H3PO4ChemicalNaNReactor3.865.07.410842.66039.9015.00000026.00.050.5320.5712.09.00000NaN53.0NaNNaN
2Swine manureCorn stalkMg(OH)2+H3PO4ChemicalNaNReactor7.365.06.615382.66039.9015.00000026.00.050.5320.575.04.00000NaN55.0NaNNaN
3Swine manureCorn stalkMg(OH)2+H3PO4ChemicalNaNReactor8.965.06.407342.66039.9015.00000026.00.050.5320.571.00.50000NaN43.0NaNNaN
4Cow manureSawdustNaNNaNNaNReactorNaNNaN7.520001.31047.1636.00000028.0NaNNaNNaN16.07.34615NaNNaNNaNNaN
5Cow manureSawdustgypsumPhysicalNaNReactor17.0NaN7.580001.140NaNNaN28.0NaNNaNNaN6.13.53846NaNNaNNaNNaN
6Sewage sludgeSawdustNaNNaNNaNReactorNaNNaN7.700002.850NaNNaN28.0NaNNaNNaN35.115.50000NaNNaNNaNNaN
7Sewage sludgeSawdustgypsumPhysicalNaNReactor17.0NaN7.400001.85035.1519.00000028.0NaNNaNNaN11.413.46150NaNNaNNaNNaN
8Cow manureSawdustNaNNaNNaNReactorNaN61.77.870000.98450.6051.42276414.0NaNNaNNaN-1.06.79000NaNNaNNaNNaN
9Cow manureSawdustgypsumPhysicalNaNReactor6.061.47.940001.12049.9044.55357114.0NaNNaNNaN4.52.95000NaNNaNNaNNaN
Material_MainMaterial_2Additive_1Additive SpeciesAdditive_2MethodApplication Rate (%DW)Initial moisture content (%)Initial pHInitial TN (%)Initial TC (%)Initial C/N (%)Time PeriodCompost volume (m³)Initial density (kg/L)Air flow (L·min⁻¹·kg⁻¹)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)
1255NaNNaNNaNNaNNaNStaticNaN76.28NaNNaN78.270000NaN49.0NaNNaNNaNNaN0.1500.328NaN0.8003.754
1256NaNNaNNaNNaNNaNStaticNaN82.52NaN3.2086.88000027.15000090.0NaNNaNNaNNaN0.4120.223NaN0.3601.312
1257NaNNaNNaNNaNNaNStaticNaN77.67NaN2.92123.79000042.39383690.0NaNNaNNaNNaN0.1280.432NaN0.2707.299
1258NaNNaNNaNNaNNaNStaticNaN72.04NaN2.60178.23000022.48202990.0NaNNaNNaNNaN0.2230.168NaN0.1808.177
1259NaNNaNNaNNaNNaNStaticNaN45.327.3514.56174.91000012.01304942.0NaNNaNNaNNaNNaN0.140NaN0.4102.818
1260NaNNaNNaNNaNNaNWindrowNaN57.908.1410.2760.37320431.37293169.0NaNNaNNaNNaNNaN0.380NaN0.25011.935
1261NaNNaNNaNNaNNaNWindrowNaN62.908.2014.8660.37320425.00672960.0NaNNaNNaNNaN0.740NaNNaNNaN0.100
1262NaNNaNNaNNaNNaNWindrowNaN62.908.2014.8660.37320425.00672960.0NaNNaNNaNNaN0.351NaNNaNNaN0.100
1263NaNNaNNaNNaNNaNReactorNaN59.669.00NaNNaNNaNNaNNaNNaNNaNNaNNaN0.168NaN0.7850.235
1264NaNNaNNaNNaNNaNReactorNaN76.008.203.8969.21000017.79177442.0NaNNaNNaNNaN1.5001.100NaN2.9405.635

Duplicate rows

Most frequently occurring

Material_MainMaterial_2Additive_1Additive SpeciesAdditive_2MethodApplication Rate (%DW)Initial moisture content (%)Initial pHInitial TN (%)Initial TC (%)Initial C/N (%)Time PeriodCompost volume (m³)Initial density (kg/L)Air flow (L·min⁻¹·kg⁻¹)TN loss (%)NH3-N loss (%)N2O-N loss (%)TC loss (%)CH4-C loss (%)CO2-C loss (%)# duplicates
27Poultry manureNaNPO43- and Mg2+ saltsChemicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN19
5Cow manureNaNmicrobiological agentBiologicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5
25Poultry manureNaNFeCl3ChemicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4
14Poultry manureSawdustNaNNaNNaNStaticNaNNaNNaNNaNNaN20.087.0NaNNaNNaN82.72NaNNaN72.20NaNNaN3
17Poultry manureSawdustNaNNaNNaNStaticNaNNaNNaNNaNNaN25.087.0NaNNaNNaN70.73NaNNaN72.40NaNNaN3
21Poultry manureSawdustNaNNaNNaNStaticNaNNaNNaNNaNNaN30.087.0NaNNaNNaN83.93NaNNaN81.94NaNNaN3
24Poultry manureNaNCaCl2ChemicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3
28Poultry manureNaNclayPhysicalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3
0Cow manurePine SawdustzeolitePhysicalNaNReactor23.0NaN7.60.6832.3347.5100.0NaNNaN35.028.27NaNNaN53.04NaNNaN2
1Cow manurePine SawdustNaNNaNNaNReactorNaNNaN7.70.8540.4147.5100.0NaNNaN35.044.15NaNNaN61.65NaNNaN2